Comparison between Berkshire and crossbreed on meat quality, and investigation of the relationship with fatty acid composition and meat quality

The pigs were in the same feeding condition according to the Korean Feeding Standard for Swine [11] for 175–185 days. Twenty pigs for each breed were randomly selected from a local slaughterhouse in Korea. A total of 40 pigs were used in the experiment. 20 Berkshire and 20 LYD pigs were slaughtered by Livestock Products Sanitation Management Act. The average slaughter weight was 105–110 kg. The Korean commercial procedures were applied during the slaughter, and the pork loin was removed from the carcass after 24 h. The pork loins were transported to the laboratory from the slaughterhouse and analyzed after refrigerating for 16 h at 4°C (2 days postmortem).

The moisture (oven drying method, 950.46) and ash (dry ashing method, 942.05) content were determined using AOAC [12], and fat content analysis was conducted using the method generated by Folch [13]. The results were expressed in % of the sample. Protein content was analyzed using a nitrogen analyzer (SpeedDigester K-425; Distillation Unit K‐350, Büchi, Flawil, Switzerland), and % nitrogen was calculated using 6.25 (conversion factor of total nitrogen to protein).

The instrumental color was measured using a colorimeter (CR-400, Konica Minolta, Tokyo, Japan) and was taken 10 times per whole muscle sample. The color value was obtained from the average. The measuring conditions were D65 illuminant and 2° standard observer, and Commission Internationale de l’Eclairage (CIE) L*, a*, and b* were determined. Before measuring, the colorimeter was calibrated using a white calibration plate (Y = 81.2; x = 0.3191; y = 0.3263).

The 3-g pork loin sample was homogenized with 27 mL of distilled water. The pH was measured using a pH meter (S20 SevenEasyTM, Geifensee, Switzerland) and calibrated to 7.00, 4.01, and 9.21 using a pH buffer. The measurement was repeated three times per sample, and the average value was utilized.

A sample from which connective tissue and visible fat were removed was cut into 3-cm³ pieces for drip loss. The experimental procedure was conducted using the method of Honikel [14], which was slightly modified. The surface of the prepared sample was lightly wiped off with a paper towel, and the initial weight was measured. The sample was hung in the middle of a plastic container, preventing contact with walls and outside air, and left to stand for 48 h at a constant temperature of 4°C then weighed. The calculation was expressed as a percentage of the difference in weight before and after standing.

The cooking loss samples were prepared in the form of steaks (2.5 cm height, 6 cm width, and 6 cm length), and after initially weighing, they were placed in a plastic bag and heated in a water bath at 72°C until the core temperature reached 70°C. At this time, the plastic bag was not sealed, and the prepared samples were observed with a thermocouple (HT-9815, Xintai Instrument, Guangdong, China). The weight was measured after heating was completed, and the calculation expressed the difference in weight before and after heating as a percentage. Drip loss and cooking loss were measured twice per sample, and the average value was used.

For WBSF, 10 cores of 1.27 cm diameter were taken from each sample in the horizontal direction of the muscle fibers after measuring the cooking loss. The measurement was tested on a universal tensile testing machine (EZ-SX, Shimadzu, Kyoto, Japan) with a 500-N load cell and a blade shear jig. The speed of the crosshead was set to 100 mm/min. The mean value of the cores was used for each sample.

Lipid extraction was conducted by Folch et al. [13], as well as saponification, methylation, and gas chromatography under learning conditions employed by Seo et al. [15]. The amount of fatty acids was expressed as a percent of total fatty acids.

Data analysis was conducted using SAS software (9.4 ver., SAS Institute, Cary, NC, USA). Data were used for the analysis of a total of 40 pigs and were expressed as averages. Twenty-three dependent variables and one independent variable were used, and a general linear model was used to test the pig breed effect. One-way analysis of variance and Duncan’s multiple range test were used to compare 23 dependent variables including fatty acid composition, proximate composition, color, pH, WBSF, drip loss, and cooking loss, which were tested with 95% significance. Factor analysis was conducted on all pork quality variables used in this study to find the main variables affecting pork quality. At this time, the pig breed effect was removed, and the proc factor was used for analysis. The principal component method was used for the initial factor extraction. The varimax was used as a rotated method to minimize the number of variables with high loadings for each factor and to simplify factor analysis. The results were expressed as a pattern plot of factors 1 and 2.

Table 1 shows the mean, maximum, minimum, standard deviation, and coefficient of variation of the proximate composition, pH, instrumental color, drip loss, cooking loss, WBSF, and fatty acid composition of the pork loin. The mean of pH, L*, and drip loss were 5.75, 52.25, and 2.11%, respectively. The L* is approximately two points higher than the criteria for classification between normal and abnormal pork loin proposed by Warner et al. [16] and Chmiel et al. [17]. However, considering the pH and drip loss, it can be judged that our pork loin was sufficiently within the normal meat range. In terms of proximate composition, fat content was approximately 3%. The fat percentage of most pork loins from pigs raised in Korea is 3% [18], which is comparable to the pork loin used in our study of common quality. C16:0, C18:0, C18:1, and C18:2 are the main fatty acids in pork loin.

Table 2 shows the effect of pig breeds on the physicochemical traits and fatty acid composition in pork loin. Moisture and fat content were significantly different according to the pig breed. The Berkshire had high moisture content while LYD had high-fat content (p < 0.05). According to a previous study, the fat content of Berkshire was lower than that of a Duroc used as a terminal sire among crossbred pigs [19,20]. In this study, the compared subject was LYD, and as a result, the findings were the same as in previous studies. Additionally, according to the authors, the moisture content was affected by the fat content. This is very helpful in understanding our results. Berkshire was higher in a* and lower in b* than LYD (p < 0.05), but L* did not significantly differ in both pig breeds (p > 0.05). Lee et al. [21] reported 8.47 as a result of measuring redness in 1,942 Berkshires, which was similar to our result. Also, Subramaniyan [3] compared meat quality characteristics between Berkshire and LYD, which was similar to our redness and b* values except for L*. Drip loss and cooking loss, which represented the water-holding capacity, were significantly higher in LYD than Berkshire (p < 0.05). Additionally, the WBSF of Berkshire was lower than that of LYD (p < 0.05). Subramaniyan et al. [3] reported that there was no significant difference in the drip loss and WBSF between Berkshire and LYD but the cooking loss was significantly lower than that of LYD.

Collectively, based on the results obtained from the physicochemical properties, the main differences are fat content, a*, pH, and water-holding capacity (drip loss and cooking loss). In addition, Barlocco et al. [22] reported a positive correlation between IMF and shear force (r = 0.31), as in our results, in experiments related to predictive models of IMF, moisture, and shear force in pork. On the other hand, Fortin et al. [23] reported a negative correlation (r = −0.47) between IMF and shear force but argued that it still needed to debate with pork tenderness and IMF levels. Therefore, considering our results, the high WBSF despite the high fat content of LYD may be due to the higher cooking loss compared to Berkshire.

The nine fatty acids were detected in our experiment of which C16:0, C17:1, C18:0, C18:2, C20:4, SFA, and polyunsaturated fatty acid (PUFA) significantly differed according to pig breed (p < 0.05). The C16:0 and C18:0 in SFA were higher in Berkshire than in LYD, and the C17:1, C18:2, and C20:4 in UFA were lower in Berkshire than in LYD. Thus, SFA was high in Berkshire whereas PUFA was low. Alonso et al. [24] analyzed the fatty acid composition in three crossbred pigs using different sire lines. Similar to our results, PUFA decreased in crossbred pigs with increased SFA, and there was no difference in MUFA in all crossbreeds. Also, C16:0 and C18:0 showed the highest significance level for the effect of the pig breed. However, in our study, C18:2 and C20:4 had the highest significance level in the PUFA, whereas Alonso [24] reported that C18:3 had the highest significance level. Pigs are monogastric animals and are more affected by diet systems than ruminants; this means that feed consumed by pigs is absorbed in the small intestine, and stored in tissues without any chemical changes, such as hydrogenation in ruminants [25]. Therefore, the differences in our experiment may have been due to feeding systems and breeding, and several previous studies have shown that differences in PUFA could be attributed to animal type or breeding [24]. This is the effect of the specification, and because our study made the same specification, the results of previous studies do not apply to us. It has been suggested that the changes in muscle C17:0 and C17:1 in pigs are due to endogenous synthesis of ingested dietary fiber derived from propionic acid produced by fermentation in the posterior intestine [25]. Also, according to Álvarez-Rodríguez et al. [26], there is a greater possibility that undigested starch will decrease than the proportion of structural carbohydrates reaching the posterior intestine. Therefore, further research should be conducted in this regard. Additionally, Cannata et al. [27] reported that C20:4 was affected by the content of IMF and increased significantly with increasing IMF. The authors found that there was a negative correlation with fat content. Although C17:1 and C20:4 occupy a small proportion of pork loin, they may be significant fatty acids in relation to meat quality.

The fatty acids showing significant differences in our sample are known as the major fatty acids in pork loin [25]. The melting points of C16:0, C17:1, C18:0, C18:2, and C20:4 showing significance were 62.9°C, 61.3°C, 69.3°C, −12°C, and −4°C, respectively. The melting point of UFAs in the subzero temperature range was lower than the melting point of SFAs detected in the experiment. In terms of processed meat, it can act as a negative factor with an increase in UFAs with a low melting point. To explain, most of the UFAs detected in pork exists in oil form. Thus, this causes the texture characteristics of pork fat to soften. Increased softening of pork fat is greatly influenced by increasing amounts of UFAs, significantly impacting overall pork texture [28].

In our fatty acid results, PUFA was significantly higher in LYD than in Berkshire by approximately 4%; this may affect shelf life in addition to the previously described aspects of pork quality characteristics. Inserra et al. [29] investigated fat oxidation and fatty acid composition of pork produced through different feeding systems, and the authors mentioned that PUFA in IMF, which can be easily oxidized, can provide information on meat oxidation. The authors also reported that PUFAs increased with an increase in the TBARS (2-thiobarbituric acid reactive substances). Therefore, an increase in PUFA may have a negative effect on the oxidation of pork lipids, leading to a decrease in storage properties.

The correlation coefficients between significant physicochemical traits are shown in Table 3. Moisture content was negatively correlated with protein, fat, and cooking loss, and it was positively correlated with drip loss. Fat content showed a positive correlation between the cooking loss and WBSF, and a negative correlation with drip loss. Additionally, WBSF showed a strong negative correlation with protein content but a positive correlation with drip loss and cooking loss. Instrumental color (L*, a*, and b*) showed a significant correlation with pH and negative correlations in all items. Additionally, b* showed a negative correlation with moisture content but a positive correlation with fat content. Taken together, proximate composition causes percent change between them, and this can affect the water-holding capacity in meat quality. Also, the color is closely related to pH, and b*, in particular, will be directly related to changes in moisture and fat content.

The correlation between fatty acids is shown in Table 4. The relationship between saturated versus UFAs in this study was confirmed. The correlation coefficients between SFA and MUFA or PUFA were −0.47 and −0.80, respectively, showing a stronger negative correlation between SFA and PUFA, while MUFA and PUFA were not significant. Additionally, in terms of individual fatty acids, C16:0 had a strong positive correlation with C18:0, and both of them showed a generally strong negative correlation with all UFAs (r = −0.47 to −0.79). The C17:1 had a negative correlation with C18:0 and C18:1 but showed a positive correlation with C18:2 and C20:4. Additionally, there was a positive correlation between C18:2 and C20:4. In summary, an increase in C16:0 led to an increase in C18:0, and an increase in SFAs leads to a decrease in UFAs. Additionally, C17:1 and C20:4 were found to be closely related to all fatty acids except for MUFAs.

Table 5 shows the correlation between physicochemical traits and fatty acid composition. C16:0 and C18:0 exhibited similar results; positive correlation with fat, cooking loss, L*, and b*; and negative correlation with drip loss and pH. Also, C16:0 exhibited a negative correlation with moisture content and a*, whereas C18:0 exhibited no significant correlation. C17:1, C18:2, and C20:4 showed opposite results with C16:0 and C18:0 and exhibited a positive correlation with moisture content and drip loss in detail, and a negative correlation with fat content, cooking loss, and b*. Additionally, C20:4 showed a correlation between pH and L*. The C18:3 was significant with pH and a*, which were negatively and positively correlated, respectively. Overall, SFA showed a positive correlation with fat content, cooking loss, L*, and pH, whereas drip loss, pH, and a* had a negative correlation with SFA. PUFA showed a positive correlation with moisture content, drip loss, and pH, whereas fat content, cooking loss, and L* had a negative correlation with PUFA. Additionally, MUFA was not significantly correlated with meat quality. This is because C16:1 and C18:1 (approximately 46%), which account for most of the detected MUFAs, did not show a correlation with meat quality.

Factor analysis was performed on all variables, and the factor loading is shown in Table 6. As a result of the rotated factor analysis, all variables of the study were classified into 5 factors, and the cumulative variance was about 97%. Factor 1 was assigned the most variables and showed an explained variance of 34.43%. SFA, C16:0, C18:0, lightness, cooking loss, C20:4 pH, PUFA, and C18:2 were classified as factor 1. In addition, SFA, C16:0, C18:0, lightness, and cooking loss showed positive factor loading, and the rest showed negative factor loading. Factor 2 was assigned MUFA and C18:1 and showed explained variance of 16.96%. Therefore, SFA, PUFA, and MUFA in factors 1 and 2 were selected as the main variables in this study considering our correlation analysis.

Partial least squares (PLS) regression analysis was performed to figure out the causation of variables and the results are shown in Fig. 1. Based on the results of factor analysis, SFA, MUFA, and PUFA were used as explanatory variables used in the PLS model, and the other variables were assigned as response variables, and the results were expressed as PLS factors 1 and 2. Figs. 1A and 1B are correlation loading plots, and the explanatory power of PLS factors 1 and 2 changed according to the role of MUFA. In view of the results of Fig. 1C, it was decided that it is appropriate to use MUFA as a response variable rather than an explanatory variable because the variable importance value of MUFA is 0.8 or less. Therefore, the PLS model is shown in Fig. 1A which uses SFA and PUFA as explanatory variables. Fig. 1A explained the results of this study well. PLS factor 1 showed an R2 value of 89.8% on the X-axis and 15.7% on the Y-axis, and PLS factor 2 showed an R2 value of 10.2% and 10.4% on the X-axis and Y-axis, respectively. Therefore, in PLS factor 1, 89.8% of the total variables can be explained by SFA and PUFA, so it is considered to be the most important factor in this study.

Fig. 1. Rotated factor pattern plot of physicochemical properties and fatty acid composition. 42.5% and 18.93% explained variance in factor 1 and factor 2, respectively. SFA, saturated fatty acid; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid.

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Consequently, SFA affects fat content, water-holding capacity, and lightness, and while PUFA affects moisture and pH. These results are thought to be affected by the composition of SFA and PUFA, and changes in SFA lead to changes in fat content. Changes in fat content would lead to changes in the proximate composition result and ultimately affect water-holding capacity. Also, changes in fat directly affect lightness by changing reflectance for light. In the PLS correlation loading plot (Fig. 1A), SFA and PUFA are in opposite positions, and when the effect of SFA is considered, PUFA have the opposite result of SFA. Thus, only the relationship between PUFA and pH should be considered. Leite et al. [30] reported that the effect of fat content was significant in pH, which was increased with increasing fat content. One possible logical explanation is that an increase in SFA will lead to an increase in fat content and ultimately an increase in pH. Therefore, PUFA shows the opposite result from SFA, so it can be considered a very appropriate interpretation.